Discriminative topological features reveal biological network mechanisms View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2004-11-22

AUTHORS

Manuel Middendorf, Etay Ziv, Carter Adams, Jen Hom, Robin Koytcheff, Chaya Levovitz, Gregory Woods, Linda Chen, Chris Wiggins

ABSTRACT

BackgroundRecent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them.ResultsWe present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate description of a given biological network. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional) "word space". This map defines an input space for classification schemes which allow us to state unambiguously which models are most descriptive of a given network of interest. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work. We show that different duplication-mutation schemes best describe the E. coli genetic network, the S. cerevisiae protein interaction network, and the C. elegans neuronal network, out of a set of network models including a linear preferential attachment model and a small-world model.ConclusionsOur method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities. More... »

PAGES

181

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-5-181

DOI

http://dx.doi.org/10.1186/1471-2105-5-181

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040330355

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15555081


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